U.S. patent number 11,419,568 [Application Number 16/432,018] was granted by the patent office on 2022-08-23 for multi-energy metal artifact reduction.
This patent grant is currently assigned to Siemens Healthcare GmbH. The grantee listed for this patent is Siemens Healthcare GmbH. Invention is credited to Christian Hofmann, Bernhard Schmidt.
United States Patent |
11,419,568 |
Hofmann , et al. |
August 23, 2022 |
Multi-energy metal artifact reduction
Abstract
A method is for metal artifact reduction in CT image data, the
CT image data including multiple 2D projection images acquired
using different projection geometries and suitable to reconstruct a
3D image data set of a volume of an imaged object. In an
embodiment, the method includes a metal artifact reduction process
including at least, acquiring, using a multi-energy CT technique,
energy-resolved CT image data associated with multiple energy
ranges. At least one result of the multi-energy technique is used
in at least one aspect of the metal artifact reduction process.
Inventors: |
Hofmann; Christian (Erlangen,
DE), Schmidt; Bernhard (Fuerth, DE) |
Applicant: |
Name |
City |
State |
Country |
Type |
Siemens Healthcare GmbH |
Erlangen |
N/A |
DE |
|
|
Assignee: |
Siemens Healthcare GmbH
(Erlangen, DE)
|
Family
ID: |
1000006512535 |
Appl.
No.: |
16/432,018 |
Filed: |
June 5, 2019 |
Prior Publication Data
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Document
Identifier |
Publication Date |
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US 20190380670 A1 |
Dec 19, 2019 |
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Foreign Application Priority Data
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Jun 13, 2018 [EP] |
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18177518 |
Mar 18, 2019 [EP] |
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19163423 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
A61B
6/032 (20130101); G06T 11/008 (20130101); A61B
6/482 (20130101); A61B 6/5258 (20130101); G06T
7/0012 (20130101); G06T 2211/408 (20130101); G16H
30/40 (20180101); G06T 2207/10081 (20130101) |
Current International
Class: |
A61B
6/00 (20060101); G16H 30/40 (20180101); G06T
7/00 (20170101); G06T 11/00 (20060101); A61B
6/03 (20060101) |
References Cited
[Referenced By]
U.S. Patent Documents
Foreign Patent Documents
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WO-2015168147 |
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Nov 2015 |
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WO |
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Other References
Meyer, Ester et al.: "Normalized Metal Artifact Reduction (NMAR) in
Computed Tomography", in: Med. Phys. 37, vol. 10, Oct. 2010, pp.
5482-5493, DOI:10.1118/1.3484090; 2010. cited by applicant .
Yu, Hengyong et al. "A Segmentation-Based Method for Metal Artifact
Reduction" Technical Report, Academic Radiology, vol. 14, No. 4,
pp. 495-504, Apr. 2007 // DOI:
https://doi.org/10.1016/j.acra.2006.12.015. cited by applicant
.
Bin Radin Nasirudin, Radin Adi Aizudin "Analysis of multi-energy
spectral CT for advanced clinical, pre-clinical, and industrial
applications" Dissertation; TU Munich; Fakultat fur Medizin, 2015
// http://medlatum.ub.tum.de?id=1233516. cited by applicant .
Extended European Search Report dated Oct. 4, 2019. cited by
applicant.
|
Primary Examiner: Le; Vu
Assistant Examiner: Nelson; Courtney Joan
Attorney, Agent or Firm: Harness, Dickey & Pierce,
P.L.C.
Claims
What is claimed is:
1. A method comprising: acquiring, using a multi-energy CT
technique, energy-resolved CT image data of a volume of an imaged
object, wherein the energy-resolved CT image data is associated
with multiple energy ranges and includes multiple 2D projection
images acquired using a plurality of projection geometries; and
segmenting metal areas of the multiple 2D projection images in at
least one of a projection domain or an image domain; correcting the
multiple 2D projection images by replacing projection image data
with interpolated data in the metal areas, after segmenting; and
reconstructing a 3D image data set from the multiple 2D projection
images, after the correcting; wherein interpolation to generate the
interpolated data is performed in the projection domain, before the
interpolation, the multiple 2D projection images are normalized
based on a forward projection of a 3D prior image determined from a
3D initial image that is reconstructed from non-normalized multiple
2D projection images, by multi-threshold segmentation, wherein the
multiple 2D projection images are denormalized after interpolation,
and portions of the energy-resolved CT image data are associated
with a respective one of the multiple energy ranges and the 3D
initial image is reconstructed from a portion of the energy
resolved CT image data associated with a highest energy range of
the multiple energy ranges.
2. The method of claim 1, wherein the segmenting is performed in
the image domain by thresholding, and the metal areas in the
multiple 2D projection images are determined by forward
projection.
3. The method of claim 2, wherein the portion of the energy
resolved CT image data associated with the highest energy range of
the multiple energy ranges is used for the segmenting of the metal
areas.
4. The method of claim 1, wherein the portion of the energy
resolved CT image data associated with the highest energy range of
the multiple energy ranges; is used for the segmenting of the metal
areas.
5. The method of claim 1, wherein spectral information determined
as a result of the multi-energy CT technique is used for refining
at least one of the multi-threshold segmentation or material
classification when determining the 3D prior image.
6. The method of claim 5, wherein the spectral information is used
to distinguish between at least one of metal and iodine or bone and
iodine to refine the at least one of the multi-threshold
segmentation or material classification when determining the 3D
prior image.
7. The method of claim 5, wherein the energy resolved CT image data
is acquired in a single scan by using a photon counting detector
using at least one energy threshold, and wherein the at least one
energy threshold is chosen depending on at least one use of the
result of the multi-energy CT technique and depending on at least
one imaging task information.
8. The method of claim 1, wherein spectral information, determined
as a result of the multi-energy CT technique, is used for at least
one of the multi-threshold segmentation or material
classification.
9. The method of claim 8, wherein the spectral information,
determined as a result of the multi-energy CT technique, is used
for definition of material specific thresholding masks.
10. The method of claim 1, wherein the energy resolved CT image
data is acquired in a single scan by using a photon counting
detector using at least one energy threshold.
11. The method of claim 1, wherein the imaged object is a
patient.
12. The method of claim 1, wherein the portion of the energy
resolved CT image data is associated with the highest energy range
of the multiple energy ranges determined using a highest threshold
in a photon counting detector.
13. A system comprising: at least one processor or a computer, to
evaluate image data, the at least processor or computer being
configured to acquire, using a multi-energy CT technique,
energy-resolved CT image data of a volume of an imaged object,
wherein the energy-resolved CT image data is associated with
multiple energy ranges and includes multiple 2D projection images
acquired using a plurality of projection geometries, segment metal
areas of the multiple 2D projection images in at least one of a
projection domain or an image domain, correct the multiple 2D
projection images by replacing projection image data with
interpolated data in the metal areas, after segmenting, and
reconstruct a 3D image data set from the multiple 2D projection
images, after the correcting, wherein interpolation to generate the
interpolated data is performed in the projection domain, before the
interpolation, the multiple 2D projection images are normalized
based on a forward projection of a 3D prior image determined from a
3D initial image that is reconstructed from non-normalized multiple
2D projection images, by multi-threshold segmentation, wherein the
multiple 2D projection images are denormalized after interpolation,
and portions of the energy-resolved CT image data are associated
with a respective one of the multiple energy ranges and the 3D
initial image is reconstructed from a portion of the energy
resolved CT image data associated with a highest energy range of
the multiple energy ranges.
14. The system of claim 13, wherein the system comprises a medical
imaging system.
15. The system of claim 14, wherein the at least one processor or
the computer are part of a network, configured to communicate with
the medical imaging system.
16. The system of claim 13, wherein the system is a computed
tomography system.
17. A non-transitory electronically readable storage medium storing
a computer program, the computer program including program elements
that, when executed by a computer, cause the computer to perform a
method comprising: acquiring, using a multi-energy CT technique,
energy-resolved CT image data of a volume of an imaged object,
wherein the energy-resolved CT image data is associated with
multiple energy ranges and includes multiple 2D projection images
acquired using a plurality of projection geometries; segmenting
metal areas of the multiple 2D projection images in at least one of
a projection domain or an image domain; correcting the multiple 2D
projection images by replacing projection image data with
interpolated data in the metal areas, after segmenting; and
reconstructing a 3D image data set from the multiple 2D projection
images, after the correcting, wherein interpolation to generate the
interpolated data is performed in the projection domain, before the
interpolation, the multiple 2D projection images are normalized
based on a forward projection of a 3D prior image determined from a
3D initial image that is reconstructed from non-normalized multiple
2D projection images, by multi-threshold segmentation, wherein the
multiple 2D projection images are denormalized after interpolation,
and portions of the energy-resolved CT image data are associated
with a respective one of the multiple energy ranges and the 3D
initial image is reconstructed from a portion of the energy
resolved CT image data associated with a highest energy range of
the multiple energy ranges.
Description
PRIORITY STATEMENT
The present application hereby claims priority under 35 U.S.C.
.sctn. 119 to European patent application numbers EP 18177518.0
filed Jun. 13, 2018, and EP 19163423.7 filed Mar. 18, 2019, the
entire contents of each of which are hereby incorporated herein by
reference.
FIELD
Embodiments of the invention generally relate to a method and
system for metal artifact reduction in computed tomography (CT)
image data. Embodiments of the invention further generally relate
to a computer program, which performs the steps of the inventive
method, if the computer program is executed on a computer, and to
an electronically readable storage medium, on which such a computer
program is stored.
BACKGROUND
Methods for reconstructing tomographic image datasets from
projection image data from a scan of an object by a CT system are
generally known. If metal items are located in the object, strong
image artifacts, known as metal artifacts, which appreciably reduce
the quality of the reconstructed image, arise on account of
increased beam hardening, more scattered radiation, a partial
volume effect and increased noise.
CT images with metal artifacts are corrupted by artifacts which can
be separated into low frequency artifacts (mainly generated by beam
hardening processes) and into high frequency (HF) artifacts (mainly
generated due to noise increase due to photon starvation and the
fact that the limited spatial resolution in the presence of very
"sharp" high attenuation structures generates
inconsistencies/streaks during reconstruction).
Current approaches established in the literature and implemented by
different vendors are based on the mechanism called "normalized
sinogram interpolation" (see, for example, the article in Med Phys.
2010 October; 37(10):5482-93; "Normalized metal artifact reduction
(NMAR) in computed tomography" by Meyer E, Raupach R, Lell M,
Schmidt B, Kachelriess M.) to replace the parts of the sinogram
which are corrupted by the metal trace.
SUMMARY
The inventors have discovered that the above-noted methods are
prone to errors in particular in cases in which the metal artifacts
are mistaken for metal objects themselves during thresholding or in
which other highly attenuating materials are present, such that
suboptimal results are achieved.
At least one embodiment of the current invention provides a
device/method for improving the performance of metal artifact
reduction algorithms for CT image data, in particular yielding
increased image quality.
Embodiments of the invention are directed to a method, a system, a
computer program and an electronically readable storage medium.
Advantageous embodiments are described by the claims.
In at least one embodiment, a metal artifact reduction process
comprises: segmenting metal areas in the projection domain and/or
the image domain, correcting the projection images by replacing
projection image data by interpolated data in the metal areas, and
reconstructing an artifact-reduced 3D image data set from the
so-corrected projection images.
An embodiment of the invention further concerns a system for metal
artifact reduction in CT image data, comprising at least one
central processing unit or a computer for the evaluation of image
data, wherein a method according to an embodiment of the invention
is implemented on the central processing unit or the computer of
the system. All remarks and comments regarding the method also
apply to the system.
Preferably, the system is or comprises a medical imaging system. An
embodiment of the invention may thus also relate to a medical
imaging system, such as a computed tomography system, which
includes a central processing unit or a computer for the evaluation
of image data, wherein a method according to an embodiment of the
invention is implemented on the central processing unit or the
computer of the medical imaging system. In this manner, all
postprocessing, that is, in particular, the whole reconstruction
pipeline, may be realized in the computed tomography apparatus
where the projection images have been acquired.
According to another embodiment of the present invention, it is
provided that components of the system are part of a network,
wherein preferably the network and a medical imaging system which
provides the image data are in communication with each other. Such
a networked solution could be implemented via an internet platform
and/or in a cloud-based computing system. In other words, the
central processing unit or the computer may be part of a network
communicating with the medical imaging system.
An embodiment of the invention further provides a computer-readable
medium on which are stored program elements that can be read and
executed by a computer in order to perform steps of a method
according to an embodiment of the invention and its various
embodiments when the program elements are executed by the
computer.
An embodiment of the invention further provides a computer program
product with program elements that can be read and executed by a
computer in order to perform steps of a method according to an
embodiment of the invention and its various embodiments when the
program elements are executed by the computer.
Further there is provided an electronically readable storage
medium, on which a computer program as described above is
stored.
BRIEF DESCRIPTION OF THE DRAWINGS
Further details and advantages of the current invention can be
taken from the following description of detailed embodiments taken
in conjunction with the drawings, in which:
FIG. 1 shows a general flow chart for embodiments of the method
according to the invention,
FIG. 2 is a flow chart of a first concrete embodiment,
FIG. 3 is a flow chart of a second concrete embodiment,
FIG. 4 is a principle drawing of an embodiment of a medical imaging
system, and
FIG. 5 shows the functional structure of a computer of a system
according to an embodiment of the invention.
DETAILED DESCRIPTION OF THE EXAMPLE EMBODIMENTS
The drawings are to be regarded as being schematic representations
and elements illustrated in the drawings are not necessarily shown
to scale. Rather, the various elements are represented such that
their function and general purpose become apparent to a person
skilled in the art. Any connection or coupling between functional
blocks, devices, components, or other physical or functional units
shown in the drawings or described herein may also be implemented
by an indirect connection or coupling. A coupling between
components may also be established over a wireless connection.
Functional blocks may be implemented in hardware, firmware,
software, or a combination thereof.
Various example embodiments will now be described more fully with
reference to the accompanying drawings in which only some example
embodiments are shown. Specific structural and functional details
disclosed herein are merely representative for purposes of
describing example embodiments. Example embodiments, however, may
be embodied in various different forms, and should not be construed
as being limited to only the illustrated embodiments. Rather, the
illustrated embodiments are provided as examples so that this
disclosure will be thorough and complete, and will fully convey the
concepts of this disclosure to those skilled in the art.
Accordingly, known processes, elements, and techniques, may not be
described with respect to some example embodiments. Unless
otherwise noted, like reference characters denote like elements
throughout the attached drawings and written description, and thus
descriptions will not be repeated. The present invention, however,
may be embodied in many alternate forms and should not be construed
as limited to only the example embodiments set forth herein.
It will be understood that, although the terms first, second, etc.
may be used herein to describe various elements, components,
regions, layers, and/or sections, these elements, components,
regions, layers, and/or sections, should not be limited by these
terms. These terms are only used to distinguish one element from
another. For example, a first element could be termed a second
element, and, similarly, a second element could be termed a first
element, without departing from the scope of example embodiments of
the present invention. As used herein, the term "and/or," includes
any and all combinations of one or more of the associated listed
items. The phrase "at least one of" has the same meaning as
"and/or".
Spatially relative terms, such as "beneath," "below," "lower,"
"under," "above," "upper," and the like, may be used herein for
ease of description to describe one element or feature's
relationship to another element(s) or feature(s) as illustrated in
the figures. It will be understood that the spatially relative
terms are intended to encompass different orientations of the
device in use or operation in addition to the orientation depicted
in the figures. For example, if the device in the figures is turned
over, elements described as "below," "beneath," or "under," other
elements or features would then be oriented "above" the other
elements or features. Thus, the example terms "below" and "under"
may encompass both an orientation of above and below. The device
may be otherwise oriented (rotated 90 degrees or at other
orientations) and the spatially relative descriptors used herein
interpreted accordingly. In addition, when an element is referred
to as being "between" two elements, the element may be the only
element between the two elements, or one or more other intervening
elements may be present.
Spatial and functional relationships between elements (for example,
between modules) are described using various terms, including
"connected," "engaged," "interfaced," and "coupled." Unless
explicitly described as being "direct," when a relationship between
first and second elements is described in the above disclosure,
that relationship encompasses a direct relationship where no other
intervening elements are present between the first and second
elements, and also an indirect relationship where one or more
intervening elements are present (either spatially or functionally)
between the first and second elements. In contrast, when an element
is referred to as being "directly" connected, engaged, interfaced,
or coupled to another element, there are no intervening elements
present. Other words used to describe the relationship between
elements should be interpreted in a like fashion (e.g., "between,"
versus "directly between," "adjacent," versus "directly adjacent,"
etc.).
The terminology used herein is for the purpose of describing
particular embodiments only and is not intended to be limiting of
example embodiments of the invention. As used herein, the singular
forms "a," "an," and "the," are intended to include the plural
forms as well, unless the context clearly indicates otherwise. As
used herein, the terms "and/or" and "at least one of" include any
and all combinations of one or more of the associated listed items.
It will be further understood that the terms "comprises,"
"comprising," "includes," and/or "including," when used herein,
specify the presence of stated features, integers, steps,
operations, elements, and/or components, but do not preclude the
presence or addition of one or more other features, integers,
steps, operations, elements, components, and/or groups thereof. As
used herein, the term "and/or" includes any and all combinations of
one or more of the associated listed items. Expressions such as "at
least one of," when preceding a list of elements, modify the entire
list of elements and do not modify the individual elements of the
list. Also, the term "example" is intended to refer to an example
or illustration.
When an element is referred to as being "on," "connected to,"
"coupled to," or "adjacent to," another element, the element may be
directly on, connected to, coupled to, or adjacent to, the other
element, or one or more other intervening elements may be present.
In contrast, when an element is referred to as being "directly on,"
"directly connected to," "directly coupled to," or "immediately
adjacent to," another element there are no intervening elements
present.
It should also be noted that in some alternative implementations,
the functions/acts noted may occur out of the order noted in the
figures. For example, two figures shown in succession may in fact
be executed substantially concurrently or may sometimes be executed
in the reverse order, depending upon the functionality/acts
involved.
Unless otherwise defined, all terms (including technical and
scientific terms) used herein have the same meaning as commonly
understood by one of ordinary skill in the art to which example
embodiments belong. It will be further understood that terms, e.g.,
those defined in commonly used dictionaries, should be interpreted
as having a meaning that is consistent with their meaning in the
context of the relevant art and will not be interpreted in an
idealized or overly formal sense unless expressly so defined
herein.
Before discussing example embodiments in more detail, it is noted
that some example embodiments may be described with reference to
acts and symbolic representations of operations (e.g., in the form
of flow charts, flow diagrams, data flow diagrams, structure
diagrams, block diagrams, etc.) that may be implemented in
conjunction with units and/or devices discussed in more detail
below. Although discussed in a particular manner, a function or
operation specified in a specific block may be performed
differently from the flow specified in a flowchart, flow diagram,
etc. For example, functions or operations illustrated as being
performed serially in two consecutive blocks may actually be
performed simultaneously, or in some cases be performed in reverse
order. Although the flowcharts describe the operations as
sequential processes, many of the operations may be performed in
parallel, concurrently or simultaneously. In addition, the order of
operations may be re-arranged. The processes may be terminated when
their operations are completed, but may also have additional steps
not included in the figure. The processes may correspond to
methods, functions, procedures, subroutines, subprograms, etc.
Specific structural and functional details disclosed herein are
merely representative for purposes of describing example
embodiments of the present invention. This invention may, however,
be embodied in many alternate forms and should not be construed as
limited to only the embodiments set forth herein.
Units and/or devices according to one or more example embodiments
may be implemented using hardware, software, and/or a combination
thereof. For example, hardware devices may be implemented using
processing circuitry such as, but not limited to, a processor,
Central Processing Unit (CPU), a controller, an arithmetic logic
unit (ALU), a digital signal processor, a microcomputer, a field
programmable gate array (FPGA), a System-on-Chip (SoC), a
programmable logic unit, a microprocessor, or any other device
capable of responding to and executing instructions in a defined
manner. Portions of the example embodiments and corresponding
detailed description may be presented in terms of software, or
algorithms and symbolic representations of operation on data bits
within a computer memory. These descriptions and representations
are the ones by which those of ordinary skill in the art
effectively convey the substance of their work to others of
ordinary skill in the art. An algorithm, as the term is used here,
and as it is used generally, is conceived to be a self-consistent
sequence of steps leading to a desired result. The steps are those
requiring physical manipulations of physical quantities. Usually,
though not necessarily, these quantities take the form of optical,
electrical, or magnetic signals capable of being stored,
transferred, combined, compared, and otherwise manipulated. It has
proven convenient at times, principally for reasons of common
usage, to refer to these signals as bits, values, elements,
symbols, characters, terms, numbers, or the like.
It should be borne in mind, however, that all of these and similar
terms are to be associated with the appropriate physical quantities
and are merely convenient labels applied to these quantities.
Unless specifically stated otherwise, or as is apparent from the
discussion, terms such as "processing" or "computing" or
"calculating" or "determining" of "displaying" or the like, refer
to the action and processes of a computer system, or similar
electronic computing device/hardware, that manipulates and
transforms data represented as physical, electronic quantities
within the computer system's registers and memories into other data
similarly represented as physical quantities within the computer
system memories or registers or other such information storage,
transmission or display devices.
In this application, including the definitions below, the term
`module` or the term `controller` may be replaced with the term
`circuit.` The term `module` may refer to, be part of, or include
processor hardware (shared, dedicated, or group) that executes code
and memory hardware (shared, dedicated, or group) that stores code
executed by the processor hardware.
The module may include one or more interface circuits. In some
examples, the interface circuits may include wired or wireless
interfaces that are connected to a local area network (LAN), the
Internet, a wide area network (WAN), or combinations thereof. The
functionality of any given module of the present disclosure may be
distributed among multiple modules that are connected via interface
circuits. For example, multiple modules may allow load balancing.
In a further example, a server (also known as remote, or cloud)
module may accomplish some functionality on behalf of a client
module.
Software may include a computer program, program code,
instructions, or some combination thereof, for independently or
collectively instructing or configuring a hardware device to
operate as desired. The computer program and/or program code may
include program or computer-readable instructions, software
components, software modules, data files, data structures, and/or
the like, capable of being implemented by one or more hardware
devices, such as one or more of the hardware devices mentioned
above. Examples of program code include both machine code produced
by a compiler and higher level program code that is executed using
an interpreter.
For example, when a hardware device is a computer processing device
(e.g., a processor, Central Processing Unit (CPU), a controller, an
arithmetic logic unit (ALU), a digital signal processor, a
microcomputer, a microprocessor, etc.), the computer processing
device may be configured to carry out program code by performing
arithmetical, logical, and input/output operations, according to
the program code. Once the program code is loaded into a computer
processing device, the computer processing device may be programmed
to perform the program code, thereby transforming the computer
processing device into a special purpose computer processing
device. In a more specific example, when the program code is loaded
into a processor, the processor becomes programmed to perform the
program code and operations corresponding thereto, thereby
transforming the processor into a special purpose processor.
Software and/or data may be embodied permanently or temporarily in
any type of machine, component, physical or virtual equipment, or
computer storage medium or device, capable of providing
instructions or data to, or being interpreted by, a hardware
device. The software also may be distributed over network coupled
computer systems so that the software is stored and executed in a
distributed fashion. In particular, for example, software and data
may be stored by one or more computer readable recording mediums,
including the tangible or non-transitory computer-readable storage
media discussed herein.
Even further, any of the disclosed methods may be embodied in the
form of a program or software. The program or software may be
stored on a non-transitory computer readable medium and is adapted
to perform any one of the aforementioned methods when run on a
computer device (a device including a processor). Thus, the
non-transitory, tangible computer readable medium, is adapted to
store information and is adapted to interact with a data processing
facility or computer device to execute the program of any of the
above mentioned embodiments and/or to perform the method of any of
the above mentioned embodiments.
Example embodiments may be described with reference to acts and
symbolic representations of operations (e.g., in the form of flow
charts, flow diagrams, data flow diagrams, structure diagrams,
block diagrams, etc.) that may be implemented in conjunction with
units and/or devices discussed in more detail below. Although
discussed in a particularly manner, a function or operation
specified in a specific block may be performed differently from the
flow specified in a flowchart, flow diagram, etc. For example,
functions or operations illustrated as being performed serially in
two consecutive blocks may actually be performed simultaneously, or
in some cases be performed in reverse order.
According to one or more example embodiments, computer processing
devices may be described as including various functional units that
perform various operations and/or functions to increase the clarity
of the description. However, computer processing devices are not
intended to be limited to these functional units. For example, in
one or more example embodiments, the various operations and/or
functions of the functional units may be performed by other ones of
the functional units. Further, the computer processing devices may
perform the operations and/or functions of the various functional
units without subdividing the operations and/or functions of the
computer processing units into these various functional units.
Units and/or devices according to one or more example embodiments
may also include one or more storage devices. The one or more
storage devices may be tangible or non-transitory computer-readable
storage media, such as random access memory (RAM), read only memory
(ROM), a permanent mass storage device (such as a disk drive),
solid state (e.g., NAND flash) device, and/or any other like data
storage mechanism capable of storing and recording data. The one or
more storage devices may be configured to store computer programs,
program code, instructions, or some combination thereof, for one or
more operating systems and/or for implementing the example
embodiments described herein. The computer programs, program code,
instructions, or some combination thereof, may also be loaded from
a separate computer readable storage medium into the one or more
storage devices and/or one or more computer processing devices
using a drive mechanism. Such separate computer readable storage
medium may include a Universal Serial Bus (USB) flash drive, a
memory stick, a Blu-ray/DVD/CD-ROM drive, a memory card, and/or
other like computer readable storage media. The computer programs,
program code, instructions, or some combination thereof, may be
loaded into the one or more storage devices and/or the one or more
computer processing devices from a remote data storage device via a
network interface, rather than via a local computer readable
storage medium. Additionally, the computer programs, program code,
instructions, or some combination thereof, may be loaded into the
one or more storage devices and/or the one or more processors from
a remote computing system that is configured to transfer and/or
distribute the computer programs, program code, instructions, or
some combination thereof, over a network. The remote computing
system may transfer and/or distribute the computer programs,
program code, instructions, or some combination thereof, via a
wired interface, an air interface, and/or any other like
medium.
The one or more hardware devices, the one or more storage devices,
and/or the computer programs, program code, instructions, or some
combination thereof, may be specially designed and constructed for
the purposes of the example embodiments, or they may be known
devices that are altered and/or modified for the purposes of
example embodiments.
A hardware device, such as a computer processing device, may run an
operating system (OS) and one or more software applications that
run on the OS. The computer processing device also may access,
store, manipulate, process, and create data in response to
execution of the software. For simplicity, one or more example
embodiments may be exemplified as a computer processing device or
processor; however, one skilled in the art will appreciate that a
hardware device may include multiple processing elements or
processors and multiple types of processing elements or processors.
For example, a hardware device may include multiple processors or a
processor and a controller. In addition, other processing
configurations are possible, such as parallel processors.
The computer programs include processor-executable instructions
that are stored on at least one non-transitory computer-readable
medium (memory). The computer programs may also include or rely on
stored data. The computer programs may encompass a basic
input/output system (BIOS) that interacts with hardware of the
special purpose computer, device drivers that interact with
particular devices of the special purpose computer, one or more
operating systems, user applications, background services,
background applications, etc. As such, the one or more processors
may be configured to execute the processor executable
instructions.
The computer programs may include: (i) descriptive text to be
parsed, such as HTML (hypertext markup language) or XML (extensible
markup language), (ii) assembly code, (iii) object code generated
from source code by a compiler, (iv) source code for execution by
an interpreter, (v) source code for compilation and execution by a
just-in-time compiler, etc. As examples only, source code may be
written using syntax from languages including C, C++, C#,
Objective-C, Haskell, Go, SQL, R, Lisp, Java.RTM., Fortran, Perl,
Pascal, Curl, OCaml, Javascript.RTM., HTML5, Ada, ASP (active
server pages), PHP, Scala, Eiffel, Smalltalk, Erlang, Ruby,
Flash.RTM., Visual Basic.RTM., Lua, and Python.RTM..
Further, at least one embodiment of the invention relates to the
non-transitory computer-readable storage medium including
electronically readable control information (processor executable
instructions) stored thereon, configured in such that when the
storage medium is used in a controller of a device, at least one
embodiment of the method may be carried out.
The computer readable medium or storage medium may be a built-in
medium installed inside a computer device main body or a removable
medium arranged so that it can be separated from the computer
device main body. The term computer-readable medium, as used
herein, does not encompass transitory electrical or electromagnetic
signals propagating through a medium (such as on a carrier wave);
the term computer-readable medium is therefore considered tangible
and non-transitory. Non-limiting examples of the non-transitory
computer-readable medium include, but are not limited to,
rewriteable non-volatile memory devices (including, for example
flash memory devices, erasable programmable read-only memory
devices, or a mask read-only memory devices); volatile memory
devices (including, for example static random access memory devices
or a dynamic random access memory devices); magnetic storage media
(including, for example an analog or digital magnetic tape or a
hard disk drive); and optical storage media (including, for example
a CD, a DVD, or a Blu-ray Disc). Examples of the media with a
built-in rewriteable non-volatile memory, include but are not
limited to memory cards; and media with a built-in ROM, including
but not limited to ROM cassettes; etc. Furthermore, various
information regarding stored images, for example, property
information, may be stored in any other form, or it may be provided
in other ways.
The term code, as used above, may include software, firmware,
and/or microcode, and may refer to programs, routines, functions,
classes, data structures, and/or objects. Shared processor hardware
encompasses a single microprocessor that executes some or all code
from multiple modules. Group processor hardware encompasses a
microprocessor that, in combination with additional
microprocessors, executes some or all code from one or more
modules. References to multiple microprocessors encompass multiple
microprocessors on discrete dies, multiple microprocessors on a
single die, multiple cores of a single microprocessor, multiple
threads of a single microprocessor, or a combination of the
above.
Shared memory hardware encompasses a single memory device that
stores some or all code from multiple modules. Group memory
hardware encompasses a memory device that, in combination with
other memory devices, stores some or all code from one or more
modules.
The term memory hardware is a subset of the term computer-readable
medium. The term computer-readable medium, as used herein, does not
encompass transitory electrical or electromagnetic signals
propagating through a medium (such as on a carrier wave); the term
computer-readable medium is therefore considered tangible and
non-transitory. Non-limiting examples of the non-transitory
computer-readable medium include, but are not limited to,
rewriteable non-volatile memory devices (including, for example
flash memory devices, erasable programmable read-only memory
devices, or a mask read-only memory devices); volatile memory
devices (including, for example static random access memory devices
or a dynamic random access memory devices); magnetic storage media
(including, for example an analog or digital magnetic tape or a
hard disk drive); and optical storage media (including, for example
a CD, a DVD, or a Blu-ray Disc). Examples of the media with a
built-in rewriteable non-volatile memory, include but are not
limited to memory cards; and media with a built-in ROM, including
but not limited to ROM cassettes; etc. Furthermore, various
information regarding stored images, for example, property
information, may be stored in any other form, or it may be provided
in other ways.
The apparatuses and methods described in this application may be
partially or fully implemented by a special purpose computer
created by configuring a general purpose computer to execute one or
more particular functions embodied in computer programs. The
functional blocks and flowchart elements described above serve as
software specifications, which can be translated into the computer
programs by the routine work of a skilled technician or
programmer.
Although described with reference to specific examples and
drawings, modifications, additions and substitutions of example
embodiments may be variously made according to the description by
those of ordinary skill in the art. For example, the described
techniques may be performed in an order different with that of the
methods described, and/or components such as the described system,
architecture, devices, circuit, and the like, may be connected or
combined to be different from the above-described methods, or
results may be appropriately achieved by other components or
equivalents.
Most of the aforementioned components, in particular the
identification unit, can be implemented in full or in part in the
form of software modules in a processor of a suitable control
device or of a processing system. An implementation largely in
software has the advantage that even control devices and/or
processing systems already in use can be easily upgraded by a
software update in order to work in the manner according to at
least one embodiment of the invention.
According to at least one embodiment of the invention, multi-energy
information is used to improve metal artifact reduction,
segmentation or imaging tasks. CT systems with the ability to
resolve different x-ray photon energies provide CT image data in a
plurality of data sets or projection image data sets which relate
to the same object. Image data sets of this type are recorded, for
example, using a plurality of energy thresholds, resulting in
multi-energy scans. In such multi-energy scans, data of a
photon-counting detector is provided with one or more energy
thresholds, wherein different data subsets are assigned to the
respective energy ranges separated by the energy thresholds. In
other words, preferably, the multi-energy CT image data is acquired
in a single scan by using a photon counting detector using at least
one energy threshold.
Alternatively, data sets of projection image data relating to
different energy ranges or energy spectra can be obtained by
scanning the object separately with x-rays of different respective
energy ranges or energy spectra. This is, however, less preferred,
since, with photon counting, CT multi-energy imaging information
can be used in every scan and thus algorithmic steps in current CT
reconstruction pipelines can make use of the multi-energy
information to improve for example the result of metal artifact
reduction algorithms as described in this document. In other words,
additional multi-energy information can be obtained without
noteworthy effort.
These energy resolved image data sets come with the advantage of
additional spectral information. This means that multi-energy based
applications can be incorporated in the reconstruction pipeline and
can be used as additional input for other algorithmic steps like
the metal artifact reduction. The following multi-energy based
applications can be used to improve algorithmic steps in metal
artifact reduction such as e.g. "normalized sinogram interpolation"
based metal artifact reduction approaches:
1. Use of optimal energy threshold for a segmentation or imaging
task.
2. Use of highest threshold (i.e. highest bin image.fwdarw.hardest
spectrum) for metal segmentation, and bone mask generation (highest
bin has least metal artifacts due to beam hardening and thus is
best suited for thresholding).
3. Use spectral information for different algorithmic steps, e.g.
masks, segmentation, or material classification to generate
material specific thresholding masks (e.g. distinguish bone and
iodine and treat them differently during thresholding). A
multi-energy evaluation can be used for the different thresholding
tasks.
As known, when working with the 2D projection images or other
information in projection space, this may be termed working in the
projection domain, as opposed to the 3D image space, the image
domain.
According to one embodiment of a method, in a first step, metal can
be segmented in the image domain by thresholding, e.g. based on the
use of the highest threshold or highest bin. An (e.g. 3D) forward
projection can identify the metal trace in the original
projections. Before interpolation, the projections can be
normalized based on a (3D) forward projection of a prior image.
This prior image can be obtained, for example, by a multithreshold
segmentation of the initial image, e.g. by using the highest
threshold. The original raw data can be divided by the projection
image data of the prior image and, after interpolation, be
denormalized again. Simulations and measurements can be performed
to compare normalized metal artifact reduction (NMAR) to standard
metal artifact reduction (MAR) with linear interpolation and MAR
based on simple length normalization.
In at least one embodiment, a metal artifact reduction process
comprises: segmenting metal areas in the projection domain and/or
the image domain, correcting the projection images by replacing
projection image data by interpolated data in the metal areas, and
reconstructing an artifact-reduced 3D image data set from the
so-corrected projection images.
In concrete embodiments, the segmentation is performed in the image
domain by thresholding, wherein the metal areas in the projection
images are determined by forward projection and interpolation is
performed in the projection domain. Any known MAR technique from
the state of the art may be employed here, in particular regarding
the interpolation step, such that these options will not be
discussed here in detail.
However, as can be shown and was surprisingly discovered during the
current invention, the highest energy range data subset of the
energy-resolved CT image data, i.e. the "highest bin" or "highest
threshold", substantially less metal artifacts are encountered.
Thus, in a first concrete advantageous embodiment, CT image data
associated with the highest energy range, in particular determined
using the highest threshold in a photon counting detector, is used
for the metal segmentation step, in particular in the image
domain.
If, thus, an initial reconstruction is performed using only the
projection image data of the highest energy range, this initial
reconstruction will have less metal artifacts, simplifying the
tasks of segmentation substantially, such that improved
segmentation results are achieved in the image domain. Of course,
if further segmentation of other high attenuation materials, for
example bone, is performed in the imaging pipeline, in particular
during the metal artifact reduction process, the highest energy
range data can, of course, also be advantageously used instead of
the full CT image data/projection image data. For example, a high
attenuation material mask, in particular a bone mask, may be
determined based on the highest energy range data.
In embodiments of the invention, NMAR as discussed above, in
particular normalized sinogram interpolation, may also be used as
the metal artifact reduction process. That is, preferably, before
the interpolation step, the projection images are normalized based
on a forward projection of a 3D prior image determined from a 3D
initial image, which is reconstructed from the originally acquired
projection images, by multi-threshold segmentation, wherein the
projection images are denormalized after interpolation. For
example, the prior image may distinguish between water-like tissue
and bone-like tissue; however, further materials and/or attenuation
value ranges may be used. In the normalization process, the result
of the multi-energy technique may also be advantageously used.
Preferably, in a first embodiment, the 3D initial image is
reconstructed from CT image data associated with the highest energy
range, in particular determined using the highest threshold in a
photon counting detector, as already explained above. Due to the
fact that such an initial image has less artifacts, a higher
quality segmentation to determine the 3D prior image is
possible.
In another advantageous embodiment, spectral information determined
as a result of the multi-energy technique may be used for refining
the multi-threshold segmentation and/or for material classification
when determining the prior image. For example, if the imaged volume
comprises bone as well as iodine or other combinations of high
attenuation materials, the energy-resolved CT image data may be
analysed to distinguish between such materials which would not be
distinguishable otherwise. For example, a multiple-materials
decomposition may be performed in areas having the same general
attenuation values. Such material decompositions in multi energy CT
are known from the state of the art.
But also generally, it is advantageous to use spectral information
determined as a result of the multi-energy technique for
segmentation and/or material classification steps, for example for
the definition of material specific thresholding masks. In
particular, if the spectral information is used to distinguish
between metal and iodine, the interpolation may be restricted to
metal areas, excluding iodine areas, and the like. In this manner,
important image information is conserved.
As already discussed, it is preferred to acquire the multi-energy
CT image data in a single scan by using a photon counting detector
using at least one energy threshold. In such a case, in
advantageous embodiments, at least one energy threshold is chosen
depending on at least one use of the result of the multi-energy
technique in the at least one step of the metal artifact reduction
process and/or depending on at least one imaging task information.
If, for example, the highest energy range data are to be used to
better segment metal and/or other high attenuation materials, the
threshold may be chosen allow as few artifacts as possible, yet
yielding enough highest energy range data in the subset to allow
for the segmentation.
In another example, if it is known that the image volume will
contain iodine, metal and bone, energy thresholds may be chosen to
allow optimal decomposition of these materials. In general, if the
imaging task information describes at least two materials in the
volume to be imaged having comparable attenuation values in
reconstructed 3D images, the energy thresholds may be chosen to
allow optimal decomposition of these materials. Of course, the
imaging task information may also describe other properties in the
volume to be imaged and/or the general objective of the
acquisition. For example, thresholds may be adjusted to the region
of interest of a body of a patient.
An embodiment of the invention further concerns a system for metal
artifact reduction in CT image data, comprising at least one
central processing unit or a computer for the evaluation of image
data, wherein a method according to an embodiment of the invention
is implemented on the central processing unit or the computer of
the system. All remarks and comments regarding the method also
apply to the system.
Preferably, the system is or comprises a medical imaging system. An
embodiment of the invention may thus also relate to a medical
imaging system, such as a computed tomography system, which
includes a central processing unit or a computer for the evaluation
of image data, wherein a method according to an embodiment of the
invention is implemented on the central processing unit or the
computer of the medical imaging system. In this manner, all
postprocessing, that is, in particular, the whole reconstruction
pipeline, may be realized in the computed tomography apparatus
where the projection images have been acquired.
According to another embodiment of the present invention, it is
provided that components of the system are part of a network,
wherein preferably the network and a medical imaging system which
provides the image data are in communication with each other. Such
a networked solution could be implemented via an internet platform
and/or in a cloud-based computing system. In other words, the
central processing unit or the computer may be part of a network
communicating with the medical imaging system.
An embodiment of the invention further provides a computer-readable
medium on which are stored program elements that can be read and
executed by a computer in order to perform steps of a method
according to an embodiment of the invention and its various
embodiments when the program elements are executed by the
computer.
An embodiment of the invention further provides a computer program
product with program elements that can be read and executed by a
computer in order to perform steps of a method according to an
embodiment of the invention and its various embodiments when the
program elements are executed by the computer.
Further there is provided an electronically readable storage
medium, on which a computer program as described above is
stored.
In the following, embodiments for improving metal artifact
reduction (MAR) in CT image data of an imaging volume of a patient
are described. These embodiments exploit the fact that a computed
tomography apparatus having a photon counting detector is used,
which can thus be controlled to acquire energy resolved CT image
data according to energy thresholds defining at least two energy
ranges. Single photon events are counted according to the energy of
the photons.
FIG. 1 shows a general overview. In a step S1, 2D projection images
are acquired using different projection geometries, for example
different projection angles along a circular acquisition
trajectory. For this imaging process, in a step S2, energy
thresholds for the photon counting detector are determined which
are best suited for the postprocessing/reconstruction pipeline. For
example, if it is known that the imaging volume comprises metal,
iodine and bone, the energy thresholds and thus energy ranges may
be chosen such that a material decomposition for those materials
has the best separation performance. In other cases or
additionally, at least the highest energy threshold may be chosen
such that as few as possible artifacts are present in a 3D
reconstruction using solely the highest energy range data, while
still providing enough image quality such that a reliable
segmentation, in particular of metal areas, may be performed.
The resulting CT image data 1 is thus comprised of subsets 2, 3
containing energy range data for respective energy ranges. While,
in this example, for reasons of simplicity only two energy ranges
and thus two subsets 2, 3 are shown, there may, of course, be
further energy thresholds and thus energy ranges defined and
used.
In a step S3, the reconstruction of a 3D image data set from the 2D
projection images of the CT image data 1 is performed. As a part of
this reconstruction pipeline, a metal artifact reduction process is
executed in step S4. At least one step of this metal artifact
reduction process uses the result of the multi-energy technique
employed to improve the artifact reduction, as indicated by joined
arrow 4 emanating from the subsets 2, 3.
FIG. 2 and FIG. 3 illustrate two concrete embodiments of the
reconstruction pipeline and metal artifact reduction.
In the embodiment of FIG. 2, conventional interpolation (without
normalization) is shown. In a step S5, using, for example, filtered
back projection (FBP), an initial 3D reconstruction or 3D initial
image is determined from the projection image data of acquired
projection images which are associated with the highest energy
range, in this case, for example, the subset 3 (which may also be
termed "highest energy range data"). Due to beam hardening, this 3D
initial image contains substantially less metal artifacts.
In a step S6, metal areas are segmented in the 3D initial image,
for example by simple thresholding, which is enabled by the
sparsity or even absence of artifacts. If the imaged volume also
contained iodine or other materials having an attenuation value
comparable to the attenuation value of metal, the energy-resolved
CT image data 1, in particular the subsets 2, 3, may also be used
for a material decomposition step allowing to distinguish between
these materials, such that a metal-only mask results.
In a step S7, the metal areas are forward projected using the
projection geometries of the projection images to yield metal areas
in the projection domain, such that, in a step S8, the projection
image data in these metal areas may be removed and an interpolated
new data may be added, such that corrected projection images
result. In a step S9, the 3D image data set is reconstructed using
the corrected projection images.
FIG. 3 shows an embodiment using normalized metal artifact
reduction as the metal artifact reducation process, which may also
be called "normalized sinogram interpolation". Regarding the
general concept, please refer to the article initially cited.
Here, steps S5, S6, and S7 are as described with respect to FIG. 2
and yield metal areas in the projection domain, however, before the
interpolation (step S8) takes place, a prior image is determined in
step S10 from the 3D initial image or another 3D initial image
(which is preferably, in this case, reconstructed using the
projection image data of all subsets 2, 3) by multi-thresholding,
in this case distinguishing bone from water-equivalent tissue.
Material decomposition evaluating the subsets 2, 3 may, again, be
used to distinguish bone from iodine or other materials having a
comparable attenuation. Would, for example, contrast agent like
iodine be classified as bone, the final correction results are
likely to have inferior image quality. Using the multi-energy
technique, here applying energy thresholds to the photon counting
detector, allows spectral analysis to exclude iodine or other
contrast agents from determining the prior image in step S10. In
particular, a classification mask may be calculated using
multiple-material decomposition, assigning, for example, "iodine"
or "bone" to respective voxels of the 3D initial image.
In a step S11, the prior image is also forward projected using the
projection geometries of the projection images to yield 2D
normalization images. These are then used in a step S12 to
normalize the projection images by pixel-wise division of the
original projection images by the respective normalization
images.
The interpolation in the metal areas of the projection domain
determined in step S7 takes place in a step S8' using the
normalized 2D images, as described in the above-cited article by E.
Meyer et al. After interpolation, in a step S13, the corrected
normalized 2D images are denormalized using the 2D normalization
images determined in step S11, in this case using multiplication.
The result are corrected projection images, which can, in a step
S9', again be used to reconstruct the artifact-reduced 3D image
data set.
In FIG. 4, a principle drawing of a medical imaging system, in this
case a computed tomography system 5, is shown. The computed
tomography apparatus comprises a gantry 6, in which an x-ray source
7 and an x-ray detector 8 are movably mounted. The x-ray detector 8
is a photon counting detector. The computed tomography system
further comprises, as a control device, a computer 9 which is
configured to perform a method according to the invention, in
particular as described above with respect to FIGS. 1 to 3.
It is noted that, in another embodiment of a system for metal
artifact reduction according to the invention, the computer may
also be provided externally to the computed tomography system 5,
for example as part of a network communicating with the medical
imaging system.
FIG. 5 shows an example functional structure of a computer of a
system according to the invention, in this case of the computer
9.
The computer 9 comprises, as known in the state of the art, a
reconstruction unit 10 for performing three-dimensional
reconstruction as in steps S5 and S9, S9', a metal segmentation
unit 11 for performing the step S6, a forward projection unit 12
for forward projection as in steps S7 and S11, an interpolation
unit 13 used in steps S8, S8', optionally a prior image determining
unit 14 and a normalization unit 15 for steps S10, S12 and S13,
respectively, and an acquisition unit 16 controlling the
acquisition of x-ray data with the computed tomography system 5, in
particular of the energy-resolved CT image data in step S1. In an
adjusting unit 17, suitable energy thresholds may be chosen
according to step S2.
The computer 9 further comprises a supplementary unit 18, adapted
for applying results of the multi-energy technique, as described,
to steps S6 and/or S10, and optionally other steps. In this case,
the supplementary unit 18 may be or comprise a material
decomposition unit. Additionally or alternatively, the
supplementary unit may restrict reconstruction by the
reconstruction unit 10 to the highest energy range data in steps S5
and/or prior to step S10.
Although the present invention has been described in detail with
reference to the preferred embodiment, the present invention is not
limited by the disclosed examples from which the skilled person is
able to derive other variations without departing from the scope of
the invention.
The patent claims of the application are formulation proposals
without prejudice for obtaining more extensive patent protection.
The applicant reserves the right to claim even further combinations
of features previously disclosed only in the description and/or
drawings.
References back that are used in dependent claims indicate the
further embodiment of the subject matter of the main claim by way
of the features of the respective dependent claim; they should not
be understood as dispensing with obtaining independent protection
of the subject matter for the combinations of features in the
referred-back dependent claims. Furthermore, with regard to
interpreting the claims, where a feature is concretized in more
specific detail in a subordinate claim, it should be assumed that
such a restriction is not present in the respective preceding
claims.
Since the subject matter of the dependent claims in relation to the
prior art on the priority date may form separate and independent
inventions, the applicant reserves the right to make them the
subject matter of independent claims or divisional declarations.
They may furthermore also contain independent inventions which have
a configuration that is independent of the subject matters of the
preceding dependent claims.
None of the elements recited in the claims are intended to be a
means-plus-function element within the meaning of 35 U.S.C. .sctn.
112(f) unless an element is expressly recited using the phrase
"means for" or, in the case of a method claim, using the phrases
"operation for" or "step for."
Example embodiments being thus described, it will be obvious that
the same may be varied in many ways. Such variations are not to be
regarded as a departure from the spirit and scope of the present
invention, and all such modifications as would be obvious to one
skilled in the art are intended to be included within the scope of
the following claims.
* * * * *
References